Size-dependent trajectories of DNA macromolecules due to insulative dielectrophoresis in submicrometer-deep fluidic channels.

In this paper, we demonstrate for the first time that insulative dielectrophoresis can induce size-dependent trajectories of DNA macromolecules. We experimentally use lambda (48.5 kbp) and T4GT7 (165.6 kbp) DNA molecules flowing continuously around a sharp corner inside fluidic channels with a depth of 0.4 mum. Numerical simulation of the electrokinetic force distribution inside the channels is in qualitative agreement with our experimentally observed trajectories. We discuss a possible physical mechanism for the DNA polarization and dielectrophoresis inside confining channels, based on the observed dielectrophoresis responses due to different DNA sizes and various electric fields applied between the inlet and the outlet. The proposed physical mechanism indicates that further extensive investigations, both theoretically and experimentally, would be very useful to better elucidate the forces involved at DNA dielectrophoresis. When applied for size-based sorting of DNA molecules, our sorting method offers two major advantages compared to earlier attempts with insulative dielectrophoresis: Its continuous operation allows for high-throughput analysis, and it only requires electric field strengths as low as approximately 10 Vcm.

Learning the Structure of Deep Sparse Graphical Models

Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is difficult. The Indian buffet process has been used as a nonparametric Bayesian prior on the directed structure of a belief network with a single infinitely wide hidden layer. In this paper, we introduce the cascading Indian buffet process (CIBP), which provides a nonparametric prior on the structure of a layered, directed belief network that is unbounded in both depth and width, yet allows tractable inference. We use the CIBP prior with the nonlinear Gaussian belief network so each unit can additionally vary its behavior between discrete and continuous representations. We provide Markov chain Monte Carlo algorithms for inference in these belief networks and explore the structures learned on several image data sets.

Dem analysis of soil-pipeline interaction in sand under lateral and upward movements at deep embedment

The soil-pipeline interactions under lateral and upward pipe movements in sand are investigated using DEM analysis. The simulations are performed for both medium and dense sand conditions at different embedment ratios of up to 60. The comparison of peak dimensionless forces from the DEM and earlier FEM analyses shows that, for medium sand, both methods show similar peak dimensionless forces. For dense sand, the DEM analysis gives more gradual transition of shallow to deep failure mechanisms than the FEM analysis and the peak dimensionless forces at very deep depth are higher in the DEM analysis than in the FEM analysis. Comparison of the deformation mechanism suggests that this is due to the differences in soil movements around the pipe associated with its particulate nature. The DEM analysis provides supplementary data of the soil-pipeline interaction in sand at deep embedment condition.